2016 Talks and Papers

For a complete list of presentations and relevant publications, please download the UCLA CGSI 2016 Program (PDF).

7/18/16
9:15-10:00
Tutorial: Brian Browning
An Introduction to Genotype Imputation
1. Marchini, J. and Howie, B., 2010. Genotype imputation for genome-wide association studies. Nature Reviews Genetics, 11(7), pp.499-511.
2. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. and Abecasis, G.R., 2012. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nature genetics, 44(8), pp.955-959.
3. Browning, B.L. and Browning, S.R., 2016. Genotype imputation with millions of reference samples. The American Journal of Human Genetics, 98(1), pp.116-126.

7/18/16
10:00-10:45
Tutorial: William (Xiaoquan) Wen
Bayesian Statistics and its Application to Integrative Statistical Genomics
1. Stephens, M. and Balding, D.J., 2009. Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), pp.681-690.
2. Wakefield, J., 2009. Bayes factors for genome‐wide association studies: comparison with P‐values. Genetic epidemiology, 33(1), pp.79-86.
3. Wen, X., Lee, Y., Luca, F. and Pique-Regi, R., 2016. Efficient integrative multi-SNP association analysis via Deterministic Approximation of Posteriors. The American Journal of Human Genetics, 98(6), pp.1114-1129.
4. Wen, X., 2016. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. The Annals of Applied Statistics, 10(3), pp.1619-1638.

7/18/16
14:00-14:45
Research Talk: Leonid Kruglyak
Genetic Basis of Complex Traits
1. Sadhu, M.J., Bloom, J.S., Day, L. and Kruglyak, L., 2016. CRISPR-directed mitotic recombination enables genetic mapping without crosses. Science, 352(6289), pp.1113-1116.
2. Bloom, J.S., Kotenko, I., Sadhu, M.J., Treusch, S., Albert, F.W. and Kruglyak, L., 2015. Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nature communications, 6.
3. Albert, F.W. and Kruglyak, L., 2015. The role of regulatory variation in complex traits and disease. Nature Reviews Genetics, 16(4), pp.197-212.
4. Albert, F.W., Treusch, S., Shockley, A.H., Bloom, J.S. and Kruglyak, L., 2014. Genetics of single-cell protein abundance variation in large yeast populations. Nature, 506(7489), pp.494-497.
5. Bloom, J.S., Ehrenreich, I.M., Loo, W.T., Lite, T.L.V. and Kruglyak, L., 2013. Finding the sources of missing heritability in a yeast cross. Nature, 494(7436), pp.234-237.

7/18/16
15:15-16:15
Research Talk: Carlos Bustamante
Interpreting Human Variation in the Personal Genome Era
1. Shringarpure, S.S. and Bustamante, C.D., 2015. Privacy risks from genomic data-sharing beacons. The American Journal of Human Genetics, 97(5), pp.631-646.
2. Mendez, F.L., Poznik, G.D., Castellano, S. and Bustamante, C.D., 2016. The divergence of Neandertal and modern human Y chromosomes. The American Journal of Human Genetics, 98(4), pp.728-734.

7/19/16
9:15-10:00
Tutorial: Alexander Schönhuth
Snakemake: Reproducible and Scalable Data Analysis
1. Köster, J. and Rahmann, S., 2012. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics, 28(19), pp.2520-2522.
2. Köster, J., 2014. Parallelization, scalability, and reproducibility in next generation sequencing analysis (Doctoral dissertation).

7/19/16
10:00-10:45
Tutorial: Jo Hardin
Tutorial on RNASeq Normalization and Differential Expression
1. Wang, Z., Gerstein, M. and Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews genetics, 10(1), pp.57-63.
2. Bullard, J.H., Purdom, E., Hansen, K.D. and Dudoit, S., 2010. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics, 11(1), p.94.
3. Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J. and Guernec, G., 2013. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in bioinformatics, 14(6), pp.671-683.
4. Lovén, J., Orlando, D.A., Sigova, A.A., Lin, C.Y., Rahl, P.B., Burge, C.B., Levens, D.L., Lee, T.I. and Young, R.A., 2012. Revisiting global gene expression analysis. Cell, 151(3), pp.476-482.

7/19/16
14:00-14:45
Tutorial: Sohini Ramachandran
Genomic Reconstructions of Deep Human History
1. Palacios, J.A., Wakeley, J. and Ramachandran, S., 2015. Bayesian nonparametric inference of population size changes from sequential genealogies. Genetics, pp.genetics-115.
2. Rasmussen, M.D., Hubisz, M.J., Gronau, I. and Siepel, A., 2014. Genome-wide inference of ancestral recombination graphs. PLoS Genet, 10(5), p.e1004342.
3. Li, H. and Durbin, R., 2011. Inference of human population history from individual whole-genome sequences. Nature, 475(7357), pp.493-496.

7/19/16
15:15-16:00
Tutorial: Alex Zelikovsky
High-Throughput Sequencing Applications to Molecular Epidemiology
1. Wertheim, J.O., Brown, A.J.L., Hepler, N.L., Mehta, S.R., Richman, D.D., Smith, D.M. and Pond, S.L.K., 2014. The global transmission network of HIV-1. Journal of Infectious Diseases, 209(2), pp.304-313.
2. Jombart, T., Cori, A., Didelot, X., Cauchemez, S., Fraser, C. and Ferguson, N., 2014. Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Comput Biol, 10(1), p.e1003457.
3. Artyomenko, A., Wu, N.C., Mangul, S., Eskin, E., Sun, R. and Zelikovsky, A., 2016, April. Long single-molecule reads can resolve the complexity of the Influenza virus composed of rare, closely related mutant variants. In International Conference on Research in Computational Molecular Biology (pp. 164-175). Springer International Publishing.
4. Skums, P., Artyomenko, A., Glebova, O., Ramachandran, S., Mandoiu, I., Campo, D.S., Dimitrova, Z., Zelikovsky, A. and Khudyakov, Y., 2014. Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling. Bioinformatics, p.btu726.
5. GHOST Makes Connections in Hepatitis C Virus Transmission. Web resource. Page last updated: October 16, 2015. Centers for Disease Control and Prevention.

7/20/16
9:15-10:00
Tutorial: Fereydoun Hormozdiari
Detecting Structural Variation
1. Hormozdiari, F., Alkan, C., Eichler, E.E. and Sahinalp, S.C., 2009. Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes. Genome research, 19(7), pp.1270-1278.
2. Alkan, C., Coe, B.P. and Eichler, E.E., 2011. Genome structural variation discovery and genotyping. Nature Reviews Genetics, 12(5), pp.363-376.
3. Handsaker, R.E., Korn, J.M., Nemesh, J. and McCarroll, S.A., 2011. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nature genetics, 43(3), pp.269-276.
4. Sindi, S.S., Önal, S., Peng, L.C., Wu, H.T. and Raphael, B.J., 2012. An integrative probabilistic model for identification of structural variation in sequencing data. Genome biology, 13(3), p.R22.
5. Rausch, T., Zichner, T., Schlattl, A., Stütz, A.M., Benes, V. and Korbel, J.O., 2012. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics, 28(18), pp.i333-i339.
6. Layer, R.M., Chiang, C., Quinlan, A.R. and Hall, I.M., 2014. LUMPY: a probabilistic framework for structural variant discovery. Genome biology, 15(6), p.R84.
7. Chaisson, M.J., Huddleston, J., Dennis, M.Y., Sudmant, P.H., Malig, M., Hormozdiari, F., Antonacci, F., Surti, U., Sandstrom, R., Boitano, M. and Landolin, J.M., 2015. Resolving the complexity of the human genome using single-molecule sequencing. Nature, 517(7536), pp.608-611.

7/20/16
10:00-10:45
Research Talk: Jason Ernst
Deciphering the Non-coding Human Genome
1. Ernst, J. and Kellis, M., 2010. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nature biotechnology, 28(8), pp.817-825.
2. Ernst, J., Kheradpour, P., Mikkelsen, T.S., Shoresh, N., Ward, L.D., Epstein, C.B., Zhang, X., Wang, L., Issner, R., Coyne, M. and Ku, M., 2011. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature, 473(7345), pp.43-49.
3. Ernst, J. and Kellis, M., 2012. ChromHMM: automating chromatin-state discovery and characterization. Nature methods, 9(3), pp.215-216.
4. Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M.J. and Amin, V., 2015. Integrative analysis of 111 reference human epigenomes. Nature, 518(7539), pp.317-330.
5. Kheradpour, P., Ernst, J., Melnikov, A., Rogov, P., Wang, L., Zhang, X., Alston, J., Mikkelsen, T.S. and Kellis, M., 2013. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome research, 23(5), pp.800-811.

7/20/16
14:00-14:45
Research Talk: Bogdan Pasaniuc
Methods to understand the Polygenic Architecture of Complex Traits
1. Shi, H., Kichaev, G. and Pasaniuc, B., 2016. Contrasting the genetic architecture of 30 complex traits from summary association data. The American Journal of Human Genetics, 99(1), pp.139-153.
2. Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B.W., Jansen, R., De Geus, E.J., Boomsma, D.I., Wright, F.A. and Sullivan, P.F., 2016. Integrative approaches for large-scale transcriptome-wide association studies. Nature genetics.
3. Kichaev, G. and Pasaniuc, B., 2015. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. The American Journal of Human Genetics, 97(2), pp.260-271.
4. Gusev, A., Shi, H., Kichaev, G., Pomerantz, M., Li, F., Long, H.W., Ingles, S.A., Kittles, R.A., Strom, S.S., Rybicki, B.A. and Nemesure, B., 2016. Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation. Nature communications, 7.

7/20/16
15:15-16:00
Tutorial: Ben Raphael
Computational Analysis of Somatic Mutations in Cancer
1. Ding, L., Wendl, M.C., McMichael, J.F. and Raphael, B.J., 2014. Expanding the computational toolbox for mining cancer genomes. Nature Reviews Genetics, 15(8), pp.556-570.
2. Raphael, B.J., Dobson, J.R., Oesper, L. and Vandin, F., 2014. Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome medicine, 6(1), p.5.
3. Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A. and Kinzler, K.W., 2013. Cancer genome landscapes. science, 339(6127), pp.1546-1558.
4. Oesper, L., Mahmoody, A. and Raphael, B.J., 2013. THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data. Genome biology, 14(7), p.R80.
5. El-Kebir, M., Oesper, L., Acheson-Field, H. and Raphael, B.J., 2015. Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics, 31(12), pp.i62-i70.

7/20/16
16:15-17:00
Research Talk: Alexander Schönhuth
Quantifying Uncertainties in Big Genome Data
1. Marschall, T., Costa, I.G., Canzar, S., Bauer, M., Klau, G.W., Schliep, A. and Schönhuth, A., 2012. CLEVER: clique-enumerating variant finder. Bioinformatics, 28(22), pp.2875-2882.
2. Marschall, T. and Schoenhuth, A., 2013. LASER: Sensitive long-Indel-aware alignment of sequencing reads. arXiv. org e-Print archive, (1303.3520).
3. Marschall, T., Hajirasouliha, I. and Schönhuth, A., 2013. MATE-CLEVER: Mendelian-inheritance-aware discovery and genotyping of midsize and long indels. Bioinformatics, p.btt556.

07/21/16
9:15-10:00
Research Talk: Jennifer Listgarten
Structured Populations in Genetics
1. Lippert, C., Listgarten, J., Liu, Y., Kadie, C.M., Davidson, R.I. and Heckerman, D., 2011. FaST linear mixed models for genome-wide association studies. Nature methods, 8(10), pp.833-835.
2. Listgarten, J., Lippert, C., Kadie, C.M., Davidson, R.I., Eskin, E. and Heckerman, D., 2012. Improved linear mixed models for genome-wide association studies. Nature methods, 9(6), pp.525-526.
3. Listgarten, J., Lippert, C. and Heckerman, D., 2013. FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nature Genetics, 45(5), pp.470-471.
4. Zou, J., Lippert, C., Heckerman, D., Aryee, M. and Listgarten, J., 2014. Epigenome-wide association studies without the need for cell-type composition. Nature methods, 11(3), pp.309-311.
5. A New Method for Deducing the Attention Span of Workshop Attendees, Journal of Visionary Research 2017
6. Also see the Fast LMM page for a full, annotated bibliography and pointers to software related to GWAS/EWAS

07/21/16
10:00-10:45
Tutorial: Saharon Rosset
Bootstrap – The Statistician’s Magic Wand
1. Efron, B. and Tibshirani, R.J., 1994. An introduction to the bootstrap. CRC press.
2. Felsenstein, J., 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution, pp.783-791.
3. Efron, B., Halloran, E. and Holmes, S., 1996. Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Sciences, 93(23), pp.13429-13429.

07/21/16
14:00-14:45
Research Talk: Noah Zaitlen
Variances and Covariances in Recently Admixed Populations
1. Zou, J.Y., Park, D.S., Burchard, E.G., Torgerson, D.G., Pino-Yanes, M., Song, Y.S., Sankararaman, S., Halperin, E. and Zaitlen, N., 2015. Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns. Proceedings of the National Academy of Sciences, 112(44), pp.13621-13626.
2. Zaitlen, N., Huntsman, S., Hu, D., Spear, M., Eng, C., Oh, S.S., White, M.J., Mak, A., Davis, A., Meade, K. and Brigino-Buenaventura, E., 2017. The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium. Genetics, 205(1), pp.375-383.
3. Park, D., Eskin, I., Kang, E.Y., Gamazon, E.R., Eng, C., Gignoux, C.R., Galanter, J.M., Burchard, E., Chun, J.Y., Aschard, H. and Eskin, E., 2016. An Ancestry Based Approach for Detecting Interactions. bioRxiv, p.036640.

07/21/16
14:45-15:30
Research Talk: Brian Browning
Genotype Imputation with Millions of Reference Samples
1. Marchini, J. and Howie, B., 2010. Genotype imputation for genome-wide association studies. Nature Reviews Genetics, 11(7), pp.499-511.
2. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. and Abecasis, G.R., 2012. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nature genetics, 44(8), pp.955-959.
3. Browning, B.L. and Browning, S.R., 2016. Genotype imputation with millions of reference samples. The American Journal of Human Genetics, 98(1), pp.116-126.

07/21/16
16:00-16:50
Research Talk: Dan Geschwind
Integrative Genomics in Neuropsychiatric Diseases
1. Parikshak, N.N., Gandal, M.J. and Geschwind, D.H., 2015. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nature Reviews Genetics, 16(8), pp.441-458.
2. Parikshak, N.N., Luo, R., Zhang, A., Won, H., Lowe, J.K., Chandran, V., Horvath, S. and Geschwind, D.H., 2013. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell, 155(5), pp.1008-1021.
3. Geschwind, D.H. and Flint, J., 2015. Genetics and genomics of psychiatric disease. Science, 349(6255), pp.1489-1494.
4. Stein, J.L., de la Torre-Ubieta, L., Tian, Y., Parikshak, N.N., Hernández, I.A., Marchetto, M.C., Baker, D.K., Lu, D., Hinman, C.R., Lowe, J.K. and Wexler, E.M., 2014. A quantitative framework to evaluate modeling of cortical development by neural stem cells. Neuron, 83(1), pp.69-86.
5. Chandran, V., Coppola, G., Nawabi, H., Omura, T., Versano, R., Huebner, E.A., Zhang, A., Costigan, M., Yekkirala, A., Barrett, L. and Blesch, A., 2016. A systems-level analysis of the peripheral nerve intrinsic axonal growth program. Neuron, 89(5), pp.956-970.

07/22/16
9:15-10:00
Tutorial: Sriram Sankararaman
Evolutionary Models in Population Genomics
1. Prüfer, K., Racimo, F., Patterson, N., Jay, F., Sankararaman, S., Sawyer, S., Heinze, A., Renaud, G., Sudmant, P.H., De Filippo, C. and Li, H., 2014. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature, 505(7481), pp.43-49.
2. Sankararaman, S., Mallick, S., Dannemann, M., Prüfer, K., Kelso, J., Pääbo, S., Patterson, N. and Reich, D., 2014. The genomic landscape of Neanderthal ancestry in present-day humans. Nature, 507(7492), pp.354-357.

7/22/16
10:00-10:45
Tutorial: Wei Wang
Alignment-free RNASeq Analysis
1. Zhang, Z. and Wang, W., 2014. RNA-Skim: a rapid method for RNA-Seq quantification at transcript level. Bioinformatics, 30(12), pp.i283-i292.

7/22/16
14:00-14:45
Research Talk: Jessica (Jingyi) Li
NMFP – Identifying mRNA Isoforms from RNASeq Data
1. Ye, Y. and Li, J.J., 2016. NMFP: a non-negative matrix factorization based preselection method to increase accuracy of identifying mRNA isoforms from RNA-seq data. BMC genomics, 17(1), p.11.

7/22/16
15:15-16:00
Research Talk: Kirk Lohmueller
The Interplay Between Demography and Selection in Dogs, Wolves, and Foxes
1. Marsden, C.D., Ortega-Del Vecchyo, D., O’Brien, D.P., Taylor, J.F., Ramirez, O., Vilà, C., Marques-Bonet, T., Schnabel, R.D., Wayne, R.K. and Lohmueller, K.E., 2016. Bottlenecks and selective sweeps during domestication have increased deleterious genetic variation in dogs. Proceedings of the National Academy of Sciences, 113(1), pp.152-157.
2. Robinson, J.A., Ortega-Del Vecchyo, D., Fan, Z., Kim, B.Y., Marsden, C.D., Lohmueller, K.E. and Wayne, R.K., 2016. Genomic flatlining in the endangered island fox. Current Biology, 26(9), pp.1183-1189.
3. Henn, B.M., Botigué, L.R., Bustamante, C.D., Clark, A.G. and Gravel, S., 2015. Estimating the mutation load in human genomes. Nature Reviews Genetics, 16(6), pp.333-343.
4. Henn, B.M., Botigue, L.R., Peischl, S., Dupanloup, I., Lipatov, M., Maples, B.K., Martin, A.R., Musharoff, S., Cann, H., Snyder, M.P. and Excoffier, L., 2016. Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. Proceedings of the National Academy of Sciences, 113(4), pp.E440-E449.

7/25/16
9:30-10:15
Tutorial: Paul Medvedev
Applying CS Tools to Biological Questions: A Case Study in Genome Assembly
1. Medvedev, P. and Brudno, M., 2009. Maximum likelihood genome assembly. Journal of computational Biology, 16(8), pp.1101-1116.

07/26/16
9:30-10:15
Tutorial: John Novembre
Models and Methods for Detecting Population Structure in Humans
1. Schraiber, J.G. and Akey, J.M., 2015. Methods and models for unravelling human evolutionary history. Nature Reviews Genetics.

07/26/16
10:15-11:00
Research Talk: Noah Rosenberg
Mathematical Bounds on Population-Genetic Statistics
1. Jakobsson, M., Edge, M.D. and Rosenberg, N.A., 2013. The relationship between FST and the frequency of the most frequent allele. Genetics, 193(2), pp.515-528.

7/27/16
9:30-10:15
Tutorial: Kenneth Lange
MM Algorithms
1. Hunter, D.R. and Lange, K., 2004. A tutorial on MM algorithms. The American Statistician, 58(1), pp.30-37.

7/28/16
9:30-10:15
Research Talk: Saharon Rosset
Mixed Modeling for Case-control Genome-wide Studies: A Major Challenge
1. Golan, D., Lander, E.S. and Rosset, S., 2014. Measuring missing heritability: inferring the contribution of common variants. Proceedings of the National Academy of Sciences, 111(49), pp.E5272-E5281.
2. Golan, D. and Rosset, S., 2014. Effective genetic-risk prediction using mixed models. The American Journal of Human Genetics, 95(4), pp.383-393.
3. Golan, D. and Rosset, S., Mixed Models for Case-Control Genome-Wide Association Studies: Major Challenges and Partial Solutions.

7/29/16
9:30-10:15
Research Talk: Jonathan Flint
The Genetics of Depression
1. Flint, J. and Kendler, K.S., 2014. The genetics of major depression. Neuron, 81(3), pp.484-503.

7/29/16
11:45-12:30
Research Talk: Yi Xing
Elucidating the Complexity of the Mammalian m⁶A Epitranscriptome
1. Molinie, B., Wang, J., Lim, K.S., Hillebrand, R., Lu, Z.X., Van Wittenberghe, N., Howard, B.D., Daneshvar, K., Mullen, A.C., Dedon, P. and Xing, Y., 2016. m6A-LAIC-seq reveals the census and complexity of the m6A epitranscriptome. Nature Methods.

8/1/16
9:30-10:15
Tutorial: Jason Ernst
Epigenome Imputation
1. Ernst, J. and Kellis, M., 2015. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nature biotechnology, 33(4), pp.364-376.
2. Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M.J. and Amin, V., 2015. Integrative analysis of 111 reference human epigenomes. Nature, 518(7539), pp.317-330.

8/1/16
10:15-11:00
Tutorial: Emilia Huerta-Sanchez
Evolutionary Adaptation in Humans
1. Nielsen, R., 2005. Molecular signatures of natural selection. Annu. Rev. Genet., 39, pp.197-218.
2. Vitti, J.J., Grossman, S.R. and Sabeti, P.C., 2013. Detecting natural selection in genomic data. Annual review of genetics, 47, pp.97-120.
3. Racimo, F., Sankararaman, S., Nielsen, R. and Huerta-Sánchez, E., 2015. Evidence for archaic adaptive introgression in humans. Nature Reviews Genetics, 16(6), pp.359-371.

8/2/16
10:30-11:15
Tutorial: Bogdan Pasaniuc
Integrative Fine-mapping for Causal Variants
1. Kichaev, G. and Pasaniuc, B., 2015. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. The American Journal of Human Genetics, 97(2), pp.260-271.
2. Kichaev, G., Yang, W.Y., Lindstrom, S., Hormozdiari, F., Eskin, E., Price, A.L., Kraft, P. and Pasaniuc, B., 2014. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet, 10(10), p.e1004722.
3. Hormozdiari, F., Kichaev, G., Yang, W.Y., Pasaniuc, B. and Eskin, E., 2015. Identification of causal genes for complex traits. Bioinformatics, 31(12), pp.i206-i213.

08/02/16
11:15-12:00
Tutorial: Noah Zaitlen
Conditioning in Association Studies
1. Zaitlen, N., Lindström, S., Pasaniuc, B., Cornelis, M., Genovese, G., Pollack, S., Barton, A., Bickeböller, H., Bowden, D.W., Eyre, S. and Freedman, B.I., 2012. Informed conditioning on clinical covariates increases power in case-control association studies. PLoS Genet, 8(11), p.e1003032.
2. Aschard, H., Vilhjalmsson, B., Patel, C., Skurnik, D., Yu, J., Wolpin, B., Kraft, P. and Zaitlen, N., 2016. Playing Musical Chairs in Big Data to Reveal Variables Associations. bioRxiv, p.057190.
3. Aschard, H., Vilhjálmsson, B.J., Joshi, A.D., Price, A.L. and Kraft, P., 2015. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. The American Journal of Human Genetics, 96(2), pp.329-339.

8/3/16
10:30-11:30
Tutorial: Sagi Snir
Tutorial on reconstructing Evolution from the Tiniest Fractions: Basics, Challenges, and Application
1. Hinchliff, C.E., Smith, S.A., Allman, J.F., Burleigh, J.G., Chaudhary, R., Coghill, L.M., Crandall, K.A., Deng, J., Drew, B.T., Gazis, R. and Gude, K., 2015. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proceedings of the National Academy of Sciences, 112(41), pp.12764-12769.
2. Snir, S. and Rao, S., 2010. Quartets maxcut: A divide and conquer quartets algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 7(4), pp.704-718.
3. Avni, E., Cohen, R. and Snir, S., 2015. Weighted quartets phylogenetics. Systematic biology, 64(2), pp.233-242.

8/4/16
10:30-11:15
Research Talk: Sagi Snir
Research Talk on reconstructing Evolution from the Tiniest Fractions: Basics, Challenges, and Application
1. Steel, M., 1992. The complexity of reconstructing trees from qualitative characters and subtrees. Journal of classification, 9(1), pp.91-116.
2. Snir, S. and Yuster, R., 2012. Reconstructing approximate phylogenetic trees from quartet samples. SIAM Journal on Computing, 41(6), pp.1466-1480.
3. Alon, N., Snir, S. and Yuster, R., 2014. On the compatibility of quartet trees. SIAM Journal on Discrete Mathematics, 28(3), pp.1493-1507.
4. Roch, S. and Snir, S., 2013. Recovering the treelike trend of evolution despite extensive lateral genetic transfer: a probabilistic analysis. Journal of Computational Biology, 20(2), pp.93-112.

08/04/16
11:15-12:15
Research Talk: Kirk Lohmueller
Comparison of the Distribution of Fitness Effects across Species using the Poisson Random Field Framework
1. Sawyer, S.A. and Hartl, D.L., 1992. Population genetics of polymorphism and divergence. Genetics, 132(4), pp.1161-1176.
2. Eyre-Walker, A. and Keightley, P.D., 2007. The distribution of fitness effects of new mutations. Nature Reviews Genetics, 8(8), pp.610-618.
3. Boyko, A.R., Williamson, S.H., Indap, A.R., Degenhardt, J.D., Hernandez, R.D., Lohmueller, K.E., Adams, M.D., Schmidt, S., Sninsky, J.J., Sunyaev, S.R. and White, T.J., 2008. Assessing the evolutionary impact of amino acid mutations in the human genome. PLoS Genet, 4(5), p.e1000083.

8/5/16
11:15-12:00
Research Talk: Sriram Sankararaman
Inferring the Structure of Archaic Admixture in Modern Humans
1. Patterson, N., Moorjani, P., Luo, Y., Mallick, S., Rohland, N., Zhan, Y., Genschoreck, T., Webster, T. and Reich, D., 2012. Ancient admixture in human history. Genetics, 192(3), pp.1065-1093.
2. Sankararaman, S., Patterson, N., Li, H., Pääbo, S. and Reich, D., 2012. The date of interbreeding between Neandertals and modern humans. PLoS Genet, 8(10), p.e1002947.
3. Sankararaman, S., Mallick, S., Dannemann, M., Prüfer, K., Kelso, J., Pääbo, S., Patterson, N. and Reich, D., 2014. The genomic landscape of Neanderthal ancestry in present-day humans. Nature, 507(7492), pp.354-357.

08/08/16
9:30-10:30
Tutorial: Barbara Engelhardt
Dimension Reduction Methods to identify Latent Structure in Genomic Data

8/8/16
10:30-11:30
Tutorial: Sriram Sankararaman
Challenges in Genomic Privacy
1. Erlich, Y. and Narayanan, A., 2014. Routes for breaching and protecting genetic privacy. Nature Reviews Genetics, 15(6), pp.409-421.
2. Gymrek, M., McGuire, A.L., Golan, D., Halperin, E. and Erlich, Y., 2013. Identifying personal genomes by surname inference. Science, 339(6117), pp.321-324.
3. Sankararaman, S., Obozinski, G., Jordan, M.I. and Halperin, E., 2009. Genomic privacy and limits of individual detection in a pool. Nature genetics, 41(9), pp.965-967.
4. Dwork, C., 2008, April. Differential privacy: A survey of results. In International Conference on Theory and Applications of Models of Computation (pp. 1-19). Springer Berlin Heidelberg.

8/9/16
9:30-10:30
Research Talk: Barbara Engelhardt
Exploring Covariation in Gene Expression Data

08/09/16
10:30-11:30
Tutorial: Kenneth Lange
Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-dimensional Data
1. Lange, K., Papp, J.C., Sinsheimer, J.S. and Sobel, E.M., 2014. Next-generation statistical genetics: modeling, penalization, and optimization in high-dimensional data. Annual review of statistics and its application, 1, pp.279-300.

08/10/16
9:30-10:30
Research Talk: Jo Hardin
Assumptions in Normalizing RNASeq Data
1. Wang, Z., Gerstein, M. and Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews genetics, 10(1), pp.57-63.
2. Bullard, J.H., Purdom, E., Hansen, K.D. and Dudoit, S., 2010. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics, 11(1), p.94.
3. Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J. and Guernec, G., 2013. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in bioinformatics, 14(6), pp.671-683.
4. Lovén, J., Orlando, D.A., Sigova, A.A., Lin, C.Y., Rahl, P.B., Burge, C.B., Levens, D.L., Lee, T.I. and Young, R.A., 2012. Revisiting global gene expression analysis. Cell, 151(3), pp.476-482.

8/10/16
15:00-16:00
Tutorial: Jonathan Flint
Quantitative Trait Locus Detection
1. Flint, J., Valdar, W., Shifman, S. and Mott, R., 2005. Strategies for mapping and cloning quantitative trait genes in rodents. Nature Reviews Genetics, 6(4), pp.271-286.

8/10/16
16:00-17:00
External (non-CGSI) Event: Kin Fai Au
UCLA Bioinformatics Lecture: Transcriptome Analysis by Hybrid Sequencing
1. Au, K.F., Sebastiano, V., Afshar, P.T., Durruthy, J.D., Lee, L., Williams, B.A., van Bakel, H., Schadt, E.E., Reijo-Pera, R.A., Underwood, J.G. and Wong, W.H., 2013. Characterization of the human ESC transcriptome by hybrid sequencing. Proceedings of the National Academy of Sciences, 110(50), pp.E4821-E4830.

08/11/16
9:30-10:30
Tutorial: David Koslicki
Bacterial Community Reconstruction via Compressed Sensing
1. Candès, E.J. and Wakin, M.B., 2008. An introduction to compressive sampling. IEEE signal processing magazine, 25(2), pp.21-30.
2. Koslicki, D., Foucart, S. and Rosen, G., 2013. Quikr: a method for rapid reconstruction of bacterial communities via compressive sensing. Bioinformatics, p.btt336.
3. Chatterjee, S., Shahrivar, D., Koslicki, D., Walker, A.W., Francis, S.C., Fraser, L.J., Vehkapera, M., Lan, Y. and Corander, J., 2005. ARK: Aggregation of Reads by K-means for Estimation of Bacterial Community Composition. PLoS One, forthcoming.
4. Chatterjee, S., Koslicki, D., Dong, S., Innocenti, N., Cheng, L., Lan, Y., Vehkaperä, M., Skoglund, M., Rasmussen, L.K., Aurell, E. and Corander, J., 2014. SEK: sparsity exploiting k-mer-based estimation of bacterial community composition. Bioinformatics, 30(17), pp.2423-2431.

08/11/16
10:30-11:30
Tutorial: Jason Ernst
Functional Genomics Time-series Analysis
1. Ernst, J., Nau, G.J. and Bar-Joseph, Z., 2005. Clustering short time series gene expression data. Bioinformatics, 21(suppl 1), pp.i159-i168.
2. Ernst, J. and Bar-Joseph, Z., 2006. STEM: a tool for the analysis of short time series gene expression data. BMC bioinformatics, 7(1), p.191.
3. Ernst, J., Vainas, O., Harbison, C.T., Simon, I. and Bar‐Joseph, Z., 2007. Reconstructing dynamic regulatory maps. Molecular Systems Biology, 3(1), p.74.
4. Bar-Joseph, Z., Gitter, A. and Simon, I., 2012. Studying and modelling dynamic biological processes using time-series gene expression data. Nature Reviews Genetics, 13(8), pp.552-564.

8/12/16
10:00-11:00
Research Talk: Joel Mefford
Linear Mixed Models – and their Equivalents